Yield Loss Research Articles

Impact of mid-Z gas fill on dynamics and performance of shock-driven implosions at the OMEGA laser.

Shock-driven implosions with 100% deuterium (D_{2}) gas fill compared to implosions with 50:50 nitrogen-deuterium (N_{2}D_{2}) gas fill have been performed at the OMEGA laser facility to test the impact of the added mid-Z fill gas on implosion performance. Ion temperature (T_{ion}) as inferred from the width of measured DD-neutron spectra is seen to be 34%±6% higher for the N_{2}D_{2} implosions than for the D_{2}-only case, while the DD-neutron yield from the D_{2}-only implosion is 7.2±0.5 times higher than from the N_{2}D_{2} gas fill. The T_{ion} enhancement for N_{2}D_{2} is observed in spite of the higher Z, which might be expected to lead to higher radiative loss, and higher shock strength for the D_{2}-only versus N_{2}D_{2} implosions due to lower mass, and is understood in terms of increased shock heating of N compared to D, heat transfer from N to D prior to burn, and limited amount of ion-electron-equilibration-mediated additional radiative loss due to the added higher-Z material. This picture is supported by interspecies equilibration timescales for these implosions, constrained by experimental observables. The one-dimensional (1D) kinetic Vlasov-Fokker-Planck code ifp and the radiation hydrodynamic simulation codes hyades (1D) and xrage [1D, two-dimensional (2D)] are brought to bear to understand the observed yield ratio. Comparing measurements and simulations, the yield loss in the N_{2}D_{2} implosions relative to the pure D_{2}-fill implosion is determined to result from the reduced amount of D_{2} in the fill (fourfold effect on yield) combined with a lower fraction of the D_{2} fuel being hot enough to burn in the N_{2}D_{2} case. The experimental yield and T_{ion} ratio observations are relatively well matched by the kinetic simulations, which suggest interspecies diffusion is responsible for the lower fraction of hot D_{2} in the N_{2}D_{2} relative to the D_{2}-only case. The simulated absolute yields are higher than measured; a comparison of 1D versus 2D xrage simulations suggest that this can be explained by dimensional effects. The hydrodynamic simulations suggest that radiative losses primarily impact the implosion edges, with ion-electron equilibration times being too long in the implosion cores. The observations of increased T_{ion} and limited additional yield loss (on top of the fourfold expected from the difference in D content) for the N_{2}D_{2} versus D_{2}-only fill suggest it is feasible to develop the platform for studying CNO-cycle-relevant nuclear reactions in a plasma environment.

DeepLeaf: Automated Plant Disease Diagnosis using Deep Learning Approach

Automated diagnosis of plant diseases is critical for ensuring food security and agricultural sustainability. In this study, we propose DeepLeaf, a novel deep-learning framework for the automated recognition and classification of plant diseases. DeepLeaf leverages convolutional neural networks to analyze plant leaf images and accurately identify disease symptoms. The framework is trained on a large dataset of annotated images, encompassing a wide range of plant species and disease types. Through extensive experimentation, we demonstrate the effectiveness of DeepLeaf in accurately diagnosing plant diseases across diverse environmental conditions and varying degrees of disease severity. Our results show that DeepLeaf achieves high accuracy and robustness, outperforming traditional methods and commercial systems in speed and reliability. Furthermore, DeepLeaf is designed to be easily deployable in real-world agricultural settings, enabling farmers and agronomists to identify and mitigate plant diseases quickly, thus refining crop yield and dropping economic losses.

Biochemical and nanotechnological approaches to combat phytoparasitic nematodes.

The foundation of most food production systems underpinning global food security is the careful management of soil resources. Embedded in the concept of soil health is the impact of diverse soil-borne pests and pathogens, and phytoparasitic nematodes represent a particular challenge. Root-knot nematodes and cyst nematodes are severe threats to agriculture, accounting for annual yield losses of US$157 billion. The control of soil-borne phytoparasitic nematodes conventionally relies on the use of chemical nematicides, which can have adverse effects on the environment and human health due to their persistence in soil, plants, and water. Nematode-resistant plants offer a promising alternative, but genetic resistance is species-dependent, limited to a few crops, and breeding and deploying resistant cultivars often takes years. Novel approaches for the control of phytoparasitic nematodes are therefore required, those that specifically target these parasites in the ground whilst minimizing the impact on the environment, agricultural ecosystems, and human health. In addition to the development of next-generation, environmentally safer nematicides, promising biochemical strategies include the combination of RNA interference (RNAi) with nanomaterials that ensure the targeted delivery and controlled release of double-stranded RNA. Genome sequencing has identified more than 75 genes in root knot and cyst nematodes that have been targeted with RNAi so far. But despite encouraging results, the delivery of dsRNA to nematodes in the soil remains inefficient. In this review article, we describe the state-of-the-art RNAi approaches targeting phytoparasitic nematodes and consider the potential benefits of nanotechnology to improve dsRNA delivery.

Contrasting Performance of Two Winter Wheat Varieties Susceptible to Leaf Rust under Diverse Pathogen Pressure, Fungicide Application, and Cultivation Practices.

This study investigated the relationship between yield, thousand kernel weight (TKW), and crude protein of soft white winter wheat-club variety (Barbee) and soft white winter wheat common variety (Zvezdana) susceptible to leaf rust and powdery mildew under different cultivation practices. Results revealed divergence in associations between yield, TKW, and crude protein loss of winter wheat varieties susceptible to obligate pathogens. Under the same level of leaf rust infection, N-input limited yield loss of the two varieties but not to the same extent. TKW loss was affected only by variety×cultivation practice and was significantly correlated with yield loss (r = -0.727, p = 0.011) and crude protein loss (r = -0.600, p = 0.05) only in club winter wheat. We suspected that Ninput affects the difference in the relationship between yield and TKW loss among varieties. Crude protein and yield loss had a low association (R2 = 18%, p = 0.05). Finally, this study indicated that more attention should be paid to the determination of pathogen pressure that triggers yield loss. It also pointed out that yield, TKW, and crude protein response to fungicides could differ in susceptible varieties. The contribution of fungicide to yield enhancement was highly associated with the specific reaction of the variety to pathogen infection rather than solely the disease level itself.

Transcriptional Comparison Reveals Differential Resistance Mechanisms between CMV-Resistant PBC688 and CMV-Susceptible G29.

The Cucumber mosaic virus (CMV) presents a significant threat to pepper cultivation worldwide, leading to substantial yield losses. We conducted a transcriptional comparative study between CMV-resistant (PBC688) and -susceptible (G29) pepper accessions to understand the mechanisms of CMV resistance. PBC688 effectively suppressed CMV proliferation and spread, while G29 exhibited higher viral accumulation. A transcriptome analysis revealed substantial differences in gene expressions between the two genotypes, particularly in pathways related to plant-pathogen interactions, MAP kinase, ribosomes, and photosynthesis. In G29, the resistance to CMV involved key genes associated with calcium-binding proteins, pathogenesis-related proteins, and disease resistance. However, in PBC688, the crucial genes contributing to CMV resistance were ribosomal and chlorophyll a-b binding proteins. Hormone signal transduction pathways, such as ethylene (ET) and abscisic acid (ABA), displayed distinct expression patterns, suggesting that CMV resistance in peppers is associated with ET and ABA. These findings deepen our understanding of CMV resistance in peppers, facilitating future research and variety improvement.

Comparative Multi-Omics Analysis of Broomcorn Millet in Response to Anthracocystis destruens Infection.

Anthracocystis destruens is the causal agent of broomcorn millet (Panicum miliaceum) smut disease, which results in serious yield losses in broomcorn millet production. However, the molecular basis underlying broomcorn millet defense against A. destruens is less understood. In this study, we investigated how broomcorn millet responds to infection by A.destruens by employing a comprehensive multi-omics approach. We examined the responses of broomcorn millet across transcriptome, metabolome, and microbiome levels. Infected leaves exhibited an upregulation of genes related to photosynthesis, accompanied by a higher accumulation of photosynthesis-related compounds and alterations in hormonal levels. However, broomcorn millet genes involved in immune response were downregulated post A. destruens infection, suggesting that A.destruens may suppress broomcorn millet immunity. In addition, we show that the immune suppression and altered host metabolism induced by A.destruens have no significant effect on the microbial community structure of broomcorn millet leaf, thus providing a new perspective for understanding the tripartite interaction between plant, pathogen, and microbiota.

Sharing the burden: Cabbage stem flea beetle pest pressure and crop damage are lower in rapeseed fields surrounded by other rapeseed crops

The cabbage stem flea beetle (Psylliodes chrysocephala) is a significant pest of rapeseed (Brassica napus). Feeding by adult P. chrysocephala can cause severe leaf damage and larval infestation can reduce stem strength, both of which impact crop growth and development, causing substantial yield losses and economic damage. The structure of the agricultural landscape can regulate herbivorous pest populations through top-down and bottom-up processes. This has shown promise in regulating the populations of other herbivorous pests, but remains relatively unexplored for P. chrysocephala. Here we investigate how the structure of the agricultural landscape influences P. chrysocephala abundance (pest pressure) and associated crop damage. We also examine the effect of the landscape on natural enemies and their ability to regulate P. chrysocephala populations. We show that P. chrysocephala populations are primarily regulated through bottom-up processes. We identify adjacency to another rapeseed crop and the total proportion of rapeseed grown in the landscape as key factors influencing beetle pressure, crop damage, and larval infestation, but find no effect of host crop proportions grown in the previous year at the examined scales up to 1 km surrounding focal crops. We also observe positive effects of crop heterogeneity and semi-natural habitat proportions on natural enemy abundance and diversity; however, these increases had no direct impact on P. chrysocephala. Bottom-up processes appear to contribute to herbivorous pest regulation by diluting beetles in the landscape, and could represent an important mechanism for sustainably managing pest populations by adapting the proportions and neighbourhoods of rapeseed crops at small to large spatial scales.

Assessment of quantitative yield losses due to chemical intervention in cabbage (Brassica oleracea var. capitata) under field conditions

Assessment of quantitative yield losses due to chemical intervention in cabbage (Brassica oleracea var. capitata) under field conditions

Identification of effector candidates in Bipolaris sorokiniana and their expression profile analysis during pathogen-wheat interactions

Spot blotch disease, caused by the fungal pathogen Bipolaris sorokiniana, poses a significant threat to global production particularly wheat, and barley due to substantial yield losses. Similar to many fungal pathogens, the infection of host plants heavily depends on the pathogen's ability to secrete effector proteins, which manipulate host defenses and aid disease progression. However, a critical knowledge gap exists in the comprehensive identification and characterization of effector candidates (ECs) in B. sorokiniana, requiring further research efforts. Therefore, study aimed to systematically identify and characterize ECs in B. sorokiniana by conducting pathogenicity tests to confirm disease symptoms on wheat leaves, utilizing a rigorous bioinformatics approach to predict ECs through sequence analysis and structural similarities, and validating effector expression profiles during infection using RNA sequencing (RNA-Seq) and quantitative real-time polymerase chain reaction (qRT-PCR). Pathogenicity testing confirmed the typical symptoms of spot blotch disease upon inoculation with B. sorokiniana. Through bioinformatics analysis, 81 ECs were identified, showing dynamic expression patterns during infection stages. Among these ECs, genes such as Cocsa1|129517, Cocsa1|141231, and Cocsa1|193443 stood out due to their different expression patterns and structural similarities, indicating their potential roles as effectors. This study offers new novel insights into the effector repertoire of B. sorokiniana and its implications for spot blotch disease management. The identified ECs present promising targets for further investigation to clarify their specific roles in fungal virulence and host immune modulation.

Evaluating Pollination and Weed Control Strategies under Mesotunnel Systems for Organic Muskmelon Production in Iowa

Bacterial wilt of cucurbits, caused by Erwinia tracheiphila, is spread by spotted (Diabrotica undeimpunctata howardi) and striped (Acalymma vittatum) cucumber beetles and results in major losses for US cucurbit (Cucurbitaceae spp.) growers. Organic growers of muskmelon (Cucumis melo) lack reliable control measures against bacterial wilt. During previous field trials in Iowa, USA, a system called mesotunnels, which are 3.5-ft-tall barriers covered with a nylon mesh insect netting, resulted in a higher marketable yield of organic ‘Athena’ muskmelon than low tunnels or noncovered plots. However, satisfactory pollination and weed control are challenging in mesotunnels because the netting covers the crop for most or all of the growing season, and economic feasibility of these systems has not been determined. Consequently, two field trials conducted in Iowa from 2020 to 2022 evaluated strategies to ensure pollination under mesotunnels in commercial-scale plots, assess effectiveness of teff (Eragrostis tef) as a living mulch for weed control in mesotunnel systems, and compare the profitability of the treatment options for organic ‘Athena’ muskmelon. The treatments used during the pollination trial were as follows: full season, in which mesotunnels remained sealed all season and bumble bees (Bombus impatiens) were added at the start of bloom for pollination; open ends, wherein both ends of the tunnels were opened at the start of bloom then reclosed 2 weeks later; and on-off-on, in which nets were removed at the start of bloom and then reinstalled 2 weeks later. The full-season treatment had significantly higher marketable yield than the other treatments in two of three trial years. Plants with the full season and open ends treatments had a bacterial wilt incidence <2.5% across all three years and similar numbers of cucumber beetles, whereas plants with the on-off-on treatment had an average bacterial wilt incidence of 11.0% and significantly more cucumber beetles. The open ends treatment had fewer bee visits to ‘Athena’ muskmelon flowers than the other treatments. In the 2-year (2021–22) weed management trial, treatments applied to the furrow between plastic-mulched rows were as follows: landscape fabric; teff seeded at 4 lb/acre and mowed 3 weeks after seeding; teff seeded at 4 lb/acre and not mowed; a control with bare ground where weeds were mowed 3 weeks after transplanting; and a bare ground control with no mowing. The landscape fabric and mowed teff treatments had statistically similar marketable yield, and mowing appeared to minimize yield losses compared with nonmowed treatments. The landscape fabric had no weeds, followed by mowed teff, mowed bare ground, and nonmowed teff. Nonmowed bare ground had the highest weed biomass. The partial budget and cost-efficiency ratio analysis indicated that the full-season treatment was the most cost-efficient pollination option for mesotunnel systems. An economic analysis of the weed management strategies showed that using teff as a living mulch in the furrows between organic ‘Athena’ muskmelon rows, coupled with timely mowing to suppress its growth, can generate revenue comparable to that of landscape fabric. Our findings suggest that organic ‘Athena’ muskmelon growers in Iowa may gain the greatest yield and soil quality benefits when mesotunnels are kept closed for the entire season, bumble bees are used for pollination, and teff (mowed 3 weeks after seeding) is used to control weeds in the furrows. Further trials integrating these pollination and weed management strategies would help validate a comprehensive approach to organic ‘Athena’ muskmelon production under mesotunnels.

Alleviation of Different Climatic Conditions by Foliar Application of Salicylic Acid and Sodium Nitroprusside and Their Interactive Effects on Pigments and Sugar Content of Maize Under Different Sowing Dates

The agricultural sector is seriously impacted by climate change, leading to potential risks to food security. In terms of global food production, maize ranks third. As a result, crop production and food security depend critically on assessing the effects of climate change and developing measures to adapt maize. Regarding adaptability, changing planting dates and using different agrochemicals are more effective than other management. Crop models are part of a global decision support system to help farmers maximize yields despite unpredictable weather patterns. To mitigate yield loss and protect the ecosystem, it is essential to use efficient maize-sowing practices in the field. This experiment was carried out to identify the most favorable sowing dates that maximize yield while ensuring the crop’s productivity and the integrity of the surrounding ecosystem remain intact. The main aim of this experiment was to mitigate the different climatic conditions by exogenous application of salicylic acid (SA) and sodium nitroprusside (SNP) on pigments and sugar content in maize under different sowing dates. A field experiment was carried out in the School of Agriculture, Lovely Professional University, Punjab, India, during the spring season of 2022. The experiment dealt with various maize crops, PMH-10, sourced from the Punjab Agricultural University (PAU), Punjab. The experiment was conducted in an open-air environment. The experimental setup was laid out in a split-plot design. The results stated that foliar application of salicylic acid and sodium nitroprusside successfully influenced high-temperature tolerance and low temperature at the reproductive phase and initial vegetative stages with other growing climatic conditions of maize in early and late sowings when controlled by increasing the chlorophyll index, carotenoids content, and sugar content of maize.

Validation and identification of promising gene specific markers governing foliar disease resistance in groundnut (Arachis hypogaea L.).

Groundnut is vulnerable to the major foliar fungal disease viz., late leaf spot (LLS) and rust in kharif season, which results in severe yield losses. Until now, LLS and rust resistance linked markers were developed based on GPBD 4 as a major donor source and were validated in its derivatives only, which restricted their use in marker assisted selection (MAS) involving other donors. The current study focused to validate LLS and rust resistance linked markers employing advanced breeding lines of F6 generation, derived from nine different crosses involving nine diverse parents, to identify potential markers for marker-assisted breeding of LLS and rust resistance in groundnut. Out of 28-trait linked markers used for validation, 8 were polymorphic (28.57%). Marker-trait association (MTA) and Single Marker Analysis (SMA) revealed that the SSR marker pPGPseq5D05 is significantly associated with both LLS (15.8% PVE) and rust (17.5% PVE) resistance, whereas, the marker IPAHM103 is tightly linked with rust resistance (26.8% PVE) alone. In silico analysis revealed that the marker gene for IPAHM103 is a zinc finger protein and the marker gene for pPGPseq5D05 is an ADP-ribosylation factor GTPase-activating protein. Both these protein products impart resistance or tolerance to biotic stress in crop plants. Two other markers namely, GMLQ975 and pPGPseq13A10 were also found to be associated with LLS resistance explaining MTA up to 60%. These gene specific markers will enable us to screen more number of germplasm lines or newly developed lines in MAS schemes for LLS and rust resistance using a wide range of resistant sources.

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

Estimation of the economic impact of heat stress on the Chilean dairy regions by using two comfort thermal indices.

The study aimed to estimate economic losses associated with heat stress in the eight dairy production regions (DPR), defined by the Dairy Chilean Consortium, using two comfort thermal indices, namely, the temperature-humidity index (THI) and the THI adjusted for solar radiation and wind speed (THIa). Hourly records from 19 weather stations (Nov - Mar 2017-2022) were collected to estimate the comfort thermal indices. The economic impact was estimated considering a critical threshold of 65 for both indices and the effect of higher values on loss in milk yield, days open, culling rate, and deaths. There were differences in the number of hours above the threshold among DPRs, independent of the thermal index used (P < 0.01). The greatest values were observed in DPRs I, II, and VIII, which concentrate most dairy cows. Average losses in milk yield were between 2.0 and 6.4 times higher when THIa was used instead of THI, which also depends on the DPR (P < 0.01). These estimations coincide with those observed empirically by producers. The lowest average economic losses per cow during the summer season (5 seasons average) occurred in DPR VI (US $ 91.5), and the highest losses were observed in DPR I (US $ 184.2) both using THIa. At the country level, economic losses fluctuate between US $ 29.0 and 108.4million per summer season, depending on the comfort thermal index used. Finally, heat stress impacts negatively and significantly the Chilean dairy sector, which is highly dependent on the DPR.

A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO[formula omitted]

The stability and control of process industries have historically faced obstacles due to inherent uncertainties in their operations. This work focuses on leveraging machine learning models as a surrogate to facilitate real-time optimization of operating conditions of the direct hydrogenation of CO2 to enhance methanol production while minimizing exergy losses. Numerous studies have explored steady-state exergy analysis and the impact of various process conditions on methanol production. However, there is a notable absence of research investigating the influence of exergy destruction on methanol production under dynamic conditions. Initially, a commercial software Aspen HYSYS was utilized to design, model, and simulate the methanol synthesis plant. Exergy analysis was conducted to quantify the process exergy losses, exergy efficiency, and potential areas for improvement. The process model transitioned to a dynamic mode by incorporating ±5% uncertainty into critical operating conditions, i.e., temperature, pressure, and molar flow rate, simulating real-world variability and resulting in a dataset of 370 samples. Two machine learning models; the Gaussian process regression (GPR) and artificial neural network (ANN) model were developed using the data samples to predict the process exergy losses and molar flow rate of methanol produced. To enhance the predictive capabilities of these deployed models, Bayesian optimization was employed for hyperparameter tuning. The developed models were employed as surrogates in multi-objective genetic algorithm (MOGA) environments with the aim of maximizing the production of methanol with minimum exergy losses under uncertainty. The optimized process conditions, derived from MOGA-based methods, underwent cross-validation using the Aspen model. This analysis revealed that the methanol synthesis plant exhibited an exergy loss of 2425 kW, an exergy efficiency of 97.78%, and an improvement potential of 53.74 kW. The GPR and ANN models demonstrated high correlation coefficients (R2) of 0.9928 and 0.9375, and root mean square error (RMSE) values of 38.93 and 1.122, respectively. Incorporating these machine learning models as surrogates within the optimization frameworks notably outperformed the base case, achieving maximum methanol production while minimizing exergy losses in the process.

Soluble and insoluble expression of recombinant human interleukin-2 protein using pET expression vector in Escherichia coli

Interleukin-2 has emerged as a potent protein-based drug to treat various cancers, AIDS, and autoimmune diseases. Despite its immense requirement, the production procedures are inefficient to meet the demand. Therefore, efficient production procedures must be adopted to improve protein yield and decrease procedural loss. This study analyzed cytoplasmic and periplasmic IL-2 expression for increased protein yield and significant biological activity. The study is focused on cloning IL-2 into a pET-SUMO and pET-28a vector that expresses IL-2 in soluble form and inclusion bodies, respectively. Both constructs were expressed into different E. coli expression strains, but the periplasmic and cytoplasmic expression of IL-2 was highest in overnight culture in Rosetta 2 (DE3). Therefore, E. coli Rosetta 2 (DE3) was selected for large-scale production and purification. Purified IL-2 was characterized by SDS-PAGE and western blotting, while its biological activity was determined using MTT bioassay. The results depict that the periplasmic and cytoplasmic IL-2 achieved adequate purification, yielding 0.86 and 0.51 mg/mL, respectively, with significant cytotoxic activity of periplasmic and cytoplasmic IL-2. Periplasmic IL-2 has shown better yield and significant biological activity in vitro which describes its attainment of native protein structure and function.

Selective Degradation of Technically Relevant Lignin to Vanillic Acid and Protocatechuic Acid.

A new and practical method for the thermal degradation of technically relevant bio-based lignin is presented. By heating a solution of lignin in highly concentrated caustic potash, vanillic acid is almost exclusively obtained in yields up to 10.6wt%. By altering the reaction parameters, the selectivity of the reaction can be shifted towards the demethylation product, protocatechuic acid, which is obtained in a yield of 6.9wt%. Furthermore, the procedure was applicable to different types of Kraft and organosolv lignin. To create an economically feasible process, ion exchange resins were used for the work-up of the highly caustic reaction media without neutralizing the complete mixture. By the selective removal of the desired vanillic acid from the caustic potash, this alkaline media could directly be reused for at least 5 further lignin degradations without significant loss of yield.

Harnessing the power of native biocontrol agents against wilt disease of Pigeonpea incited by Fusarium udum

Fusarium wilt, caused by (Fusarium udum Butler), is a significant threat to pigeonpea crops worldwide, leading to substantial yield losses. Traditional approaches like fungicides and resistant cultivars are not practical due to the persistent and evolving nature of the pathogen. Therefore, native biocontrol agents are considered to be more sustainable solution, as they adapt well to local soil and climatic conditions. In this study, five isolates of F. udum infecting pigeonpea were isolated from various cultivars and characterized morphologically and molecularly. The isolate from the ICP 8858 cultivar displayed the highest virulence of 90%. Besides, 100 endophytic bacteria, 100 rhizosphere bacteria and three Trichoderma spp. were isolated and tested against F. udum isolated from ICP 8858 under in vitro conditions. Out of the 200 bacteria tested, nine showed highest inhibition, including Rb-4 (Bacillus sp.), Rb-11 (B. subtilis), Rb-14 (B. megaterium), Rb-18 (B. subtilis), Rb-19 (B. velezensis), Eb-8 (Bacillus sp.), Eb-11 (B. subtilis), Eb-13 (P. aeruginosa), and Eb-21 (P. aeruginosa). Similarly, Trichoderma spp. were identified as T. harzianum, T. asperellum and Trichoderma sp. Notably, Rb-18 (B. subtilis) and Eb-21 (P. aeruginosa) exhibited promising characteristics such as the production of hydrogen cyanide (HCN), cellulase, siderophores, ammonia and nutrient solubilization. Furthermore, treating pigeonpea seedlings with these beneficial microorganisms led to increased levels of key enzymes (POD, PPO, and PAL) associated with resistance to Fusarium wilt, compared to untreated controls. In field trials conducted for four seasons, the application of these potential biocontrol agents as seed treatments on the susceptible ICP2376 cultivar led to the lowest disease incidence. Specifically, treatments T2 (33.33) (P. aeruginosa) and T3 (35.41) (T. harzianium) exhibited the lowest disease incidence, followed by T6 (36.5) (Carbendizim), T1 (36.66) (B. subtilis), T4 (52.91) (T. asperellum) and T5 (53.33) (Trichoderma sp.). Results of this study revealed that, P. aeruginosa (Eb-21), B. subtilis (Rb-18) and T. harzianum can be used for plant growth promotion and management of Fusarium wilt of pigeonpea.

Weed Biology and Ecology Studies: Diversity, Dominance and Prediction of Yield Loss of Corn (Zea mays L.) Due to Broadleaf Weeds Competition in Dryland

The aim of the research was to determine the diversity, distribution, dominance, and yield loss of corn due to competition from broadleaf weeds. Descriptive research method with direct survey techniques of research objects. Data were collected using 10 sample plots measuring 1 m2, which were placed using random sampling. Observation parameters include the population and dry biomass weight of weeds and corn. The data was analyzed descriptively and comparatively by calculating density, frequency, and dominance to obtain an important value index and summation diminance ratio (SDR). The results of the research showed that 14 species of broad-leaved weeds were found with high species diversity, distribution, dominance, and abundance indices. The competitiveness and relative weighted dominance of six weed species, namely Amaranthus spinusus, Amaranthus gracilis, Synedrella nodiliflora, Acalypha indica, Ageratum conyzoides, and Alternanthera philoxeroides, are higher than those of other weed species, causing corn yield losses of 5.56%–21.90%. Therefore, these six weed species must be controlled.

Farmers' biodiversity knowledge improves natural enemy conservation in agricultural ecosystems

ContextIn the face of dual challenges of sustainable food production and biodiversity conservation, the knowledge gaps of farmers about biodiversity, pests, and their natural enemies can become a crucial barrier to adopting sustainable land management practices. ObjectiveThe study aimed to assess farmers' knowledge and understanding of crop pest and natural enemy diversity and their impacts, and whether the adoption of Climate-Smart or Conservation Agriculture (CSA here onwards) was associated with this knowledge. MethodsWe conducted questionnaire-based interviews and showed biodiversity images, including crop pests and beneficial insects, to understand farmers' knowledge of biodiversity on their farms. A comparison was drawn between farmers practising CSA and non-CSA farmers to understand the role of CSA in enhancing biodiversity knowledge. Results and conclusionsFarmers facing greater yield losses were aware of pests, but they were less knowledgeable about their natural enemies, and they used fewer conservation management practices. CSA farmers, however, showed more biodiversity knowledge, especially of natural enemies, and they employed a wider range of CS management practices. Farmers’ age, experience, education, and training were positively correlated with biodiversity knowledge, leading to better natural enemy conservation and pest management practices. SignificanceThe findings underscore the need for biodiversity-focused capacity building in sustainable agricultural programs, targeting less knowledgeable farming groups. It emphasises the crucial role of farmers' knowledge in developing sustainable and biodiversity-friendly food production systems.Africa, East Usambara Mountains, Food security, Biodiversity conservation, Invertebrates, Crop pests, Climate-Smart Agriculture, Farmer education.